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Differentially private (DP) training preserves the data privacy usually at the cost of slower convergence (and thus lower accuracy), as well as more severe mis-calibration than its non-private counterpart.
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 1901
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 1904
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The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Broken promises of privacy: Responding to the surprising failure of anonymization
Paul Ohm · 2009
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Unique in the crowd: The privacy bounds of human mobility
Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel · 2013
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Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Unique in the shopping mall: On the reidentifiability of credit card metadata
Yves-Alexandre De Montjoye, Laura Radaelli, Vivek Kumar Singh, et al · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Adding gradient noise improves learning for very deep networks
Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Adaptive laplace mechanism: Differential privacy preservation in deep learning
NhatHai Phan, Xintao Wu, Han Hu, and Dejing Dou · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Revealed: 50 million facebook profiles harvested for cambridge analytica in major data breach
Understanding gradient clipping in private sgd: A geometric perspective
Xiangyi Chen, Steven Z Wu, and Mingyi Hong · 2020
Later among the works it cites.
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M Roy, and Surya Ganguli · 2020
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Computing tight differential privacy guarantees using fft
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
Later among the works it cites.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson · 2020
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A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima
Zeke Xie, Issei Sato, and Masashi Sugiyama · 2020
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Carole Cadwalladr and Emma Graham-Harrison · 2018
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Deep learning with gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J Su · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
Later among the works it cites.
Gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2020
Later among the works it cites.
Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
Closest in time.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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Differentially private bayesian neural networks on accuracy, privacy and reliability
Qiyiwen Zhang, Zhiqi Bu, Kan Chen, and Qi Long · 2021
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Differentially private bias-term only fine-tuning of foundation models
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis · 2022
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Automatic clipping: Differentially private deep learning made easier and stronger
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Disparate impact in differential privacy from gradient misalignment
Maria S Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C Cresswell · 2022
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Toward training at imagenet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori B Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin-Tat Lee, and Abhradeep Guha Thakurta · 2022
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Large scale transfer learning for differentially private image classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky · 2022
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Analytical composition of differential privacy via the edgeworth accountant
Hua Wang, Sheng Gao, Huanyu Zhang, Milan Shen, and Weijie J Su · 2022
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A closer look at the calibration of differentially private learners
Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos, and Tatsunori Hashimoto · 2022
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